Thin sections of geologic samples less than 60 .mu.m thick are customarily examined with petrographic image analysis techniques to study the micro-features of the sample. From the analysis, geological interpretations of the depositional and post-depositional processes which formed the sample can be derived. Moreover, physical qualities such as the porosity and permeability of the sample can be measured. For example, the porosity of a sample is estimated from the percentage of pore space in the image area. The permeability of the sample, which depends on an interconnected network of individual pores, can be estimated from the porosity of the sample by a linear regression formula or by the Kozeny-Carman equation.
The manual analysis of thin section images is termed point counting. In this technique, a technician reviews the magnified image of a thin section and classifies the features of the pore space and mineral grains in the image. Because point counting is labor-intensive, tedious, and is subject to errors by the technician, various techniques have been proposed to automate the processing of thin section images. Many of these techniques are based on thresholding. To apply thresholding techniques, a scanning electron microscope scans a sample and assesses the backscattered electrons from the sample to determine the brightness of each pixel in the field of view. Next, an image of the thin section is prepared and is represented by a matrix of pixels. Each pixel in the matrix is classified as either pore space or grain space by comparing the brightness of the pixel to a selected threshold level. Thresholding techniques are based on the assumption that all pixels brighter than the threshold value are part of the grain space, and all pixels darker than the threshold value are part of the pore space.
The accuracy of thresholding techniques is limited by several factors. The threshold value is determined by trial and error and may not accurately separate pore space from the grain space in the sample. If the sample contains dark grains which reflect or transmit less light than the pore space, the thresholding technique may erroneously classify the dark or non-reflecting grains as pore space. If the thin section is backlit during the analysis, certain grains in the sample may appear darker than the pore space due to polarization effects of the light as it is transmitted through the thin section. Moreover, the data furnished by thresholding techniques is limited because the pixels are not correlated with adjacent pixels. Additional processing is required to ascertain the qualitative factors represented by the image of the thin section.
A need exists for a method and system which efficiently analyzes the discrete features of a thin section. The method and system should be applicable to the micro-analysis of geologic samples and other porous compounds.